TEAM: Yu Liu, Ximing Zhong, Que Guan, Jiaxin Tao
CATEGORY: Exhibition

Mechanical AI can perform hundreds of different scenario designs. Human design perception can be wrapped in a machine. Design practice can become modular and accessible through tools. The line between instrumental and architectural knowledge is becoming increasingly blurred.

Tool-encoded knowledge actually represents a new collective cultural memory.

——MEMEX 1945 



——MEMEX 1945 


Conceptual introduction and theoretical framework

 As the change and deterioration of the global climate and environment, more and more people are concerning about the current human condition, and constantly proposing various sustainable response strategies to solve existing problems and future situations, to seek a balanced and optimized solution strategy. From the point of view of artificial intelligence, we attempt to explore experiments in which artificial intelligence imitates, learns, optimizes, and rapidly generates solutions. Through testing and predicting the climatic environment of the site and learning from high-quality examples, we are able to set different environmental and climatic conditions and needs, and quickly generate sustainable solutions for different geographical environments, site characteristics, climatic types, the relationship between architecture and landscape, and future extensible states. 

During the cloning process, whether the Dolly sheep of architectural design could preserve human intelligence? Could we integrate a cloning and adaptive framework to migrate the order of human decision-making to meet new design solutions and new contexts, where there is decision making, there is the hustle and bustle, and how could we avoid it? Who is more suitable for cloning the decision-making part of human intelligence, machine learning or probabilistic mathematical methods, and we try to explore the deep mathematical and probabilistic logic behind the form, and for machine learning, what theoretical foundations we need to generate valid parts. Through the massive and rapid self-learning, solution optimization, and complex calculations of artificial intelligence, it is hoped that more effective outcome prediction and faster and more rational sustainable solutions will be made in urban new and renovation environments.



Material Dimensions and Construction Costs

The whole installation is divided into a central tower and standard units around it, with a height of 2.5m and a length and width of 1m. The materials used include: steel/wood structure, density board, painted aluminium tubes, wire, light strips, spotlights, iron display stands, etc. The overall cost estimate (materials + labour) is around RMB 50,000.


Part II

Analysis Chart

Artificial intelligence defines the semantics by learning landscape cases under different climatic conditions, and generates the learned semantics appropriately in the new site context through the given site context.


We visualised the design and thinking logic of the AI in a 4m x 4m site, cloning and evolving the abstract ai to communicate it physically in the installation, which is divided into a central tower and surrounding standard units, with the surrounding units being cases sampled by the AI, which are filtered by wind and sunlight simulations. The AI learns from the surrounding landscape design solutions and integrates them into the tower as input. These input scenarios are integrated into a single reference scenario.The AI processing unit from bottom to top is shown learning cases, design language definition, design element migration evolution, design element reorganisation and new solution generation. From top to bottom the nodes and the complex computational processes of AI machine thinking are shown. The different colours in the installation represent different design semantics. This is expressed in different colours by way of copper pipes and layered so that the form presented in the lower half of the tower can be seen, while in the centre of the tower the AI performs intelligent solution generation based on the layout and needs of the new site. The spatial semantics that the AI learns are migrated and evolved in the new site, so here we connect and position the same spatial semantics with coloured copper wire, and a new spatial result is generated, with the final new solution presented at the top.

我们在4米×4米的场地中,我们可视化了AI的设计和思考逻辑,把抽象的ai 克隆和进化的用装置实体传达,整个装置分为了中心的高塔和周围的标准单元,周围单元为AI采样的案例,这些案例经过风模拟和阳光模拟分析筛选。 AI从周围的景观设计方案中学习整合到塔中作为输入。这些输入方案被整合成一张参考方案。AI处理装置自下而上分别是学习案例,设计语言定义,设计元素迁移进化,设计元素重组,新方案生成。从上到下展示了AI机器思考的节点和复杂的计算过程。 装置中不同的颜色代表不同的设计语意。 通过铜管的方式用不同颜色表达出来,并将其分层,因此可以看到高塔下半部分呈现的形式,而在高塔的中央,AI根据新场地的布局和需求,进行了智能化方案生成。 AI学习的空间语义在新场地中做了迁移和进化,因此这里我们用彩色铜丝将同一空间语义进行连接定位,便产生了新的空间结果,最终新方案在顶部呈现出来。

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